Energy dispersion x-ray fluorescence analysis of chemical subtances

ABSTRACT

The invention relates to a method for classifying and identifying by means of energy dispersion X-ray fluorescence analysis chemical substances whose X-ray fluorescence lines cannot be detected and which therefore cannot be classified by energy dispersion X-ray fluorescence analysis alone. Said method is characterized in that the sample to be analyzed is analyzed in its original packaging or natural state without prior processing in a sample vessel. According to the method the sample is: a) positioned in front of the measuring aperture in a sample chamber of an X-ray fluorescence apparatus; b) measured; and c) classified and identified by application of multivariate, statistical techniques to the measurement signals obtained, i.e., to the Compton and Rayleigh scattering.

The present invention relates to the discrimination and classification,by means of X-ray fluorescence analysis, of chemical substances whoseX-ray fluorescence lines cannot be detected and which therefore cannotbe classified by energy dispersive X-ray fluorescence analysis (EDXRFA)alone, through the packaging and without having to take a sample.

A fast check of the identity of laboratory chemicals is necessary inmany situations. This is primarily true in chemical companies within thecontext of so-called returned material stream management. In this case,returned material stream means chemicals which are taken back afterhaving been returned to the chemical works by end-users or wholesalers.

Under the circular economy law, every returned material stream is to beregarded as waste until a control procedure has plausibly characterizedevery returned material stream. Only then can the returned materialstream be categorized as a product, or raw material, secondary rawmaterial or definitively as waste.

After the chemicals have been taken back, they are correspondinglydocumented, inspected in terms of their composition and then re-used assecondary raw materials in production where appropriate.

Energy dispersive X-ray fluorescence analysis (EDXRFA) is a fastanalysis method for qualitative and quantitative determination ofelements in substances. The determination is carried out via evaluationof the X-ray fluorescence lines. The X-ray fluorescence lines ofelements with atomic numbers between 21 and 92 can be detected through aPE (polyethylene) container and allocated. However, elements with atomicnumbers between 1 and 20 make up a large proportion of substances. It isnot possible to characterize these elements, and therefore thesesubstances, using conventional EDXRFA evaluation (X-ray fluorescenceline determination and evaluation) owing to the lack of X-rayfluorescence lines.

Information about the substance and its composition can be obtained viathe coherent (Rayleigh scattering) and incoherent (Compton scattering)scattering of X-rays in the substance. Correlations between the averageatomic number and the ratio between coherent and incoherent scatteredradiation are known, and are described for example by H. Kunzendorf inNuclear Instruments and methods, 99 (1972) 611-612. The matrixcorrection, based on inelastic scattered radiation, for X-rayfluorescence lines is used by various EDXRFA suppliers for quantativeevaluation.

It is therefore an object of the present invention to characterize anddiscriminate from one another, without risk and without other additionalanalysis methods and without having to take a sample, substances whoseX-ray fluorescence lines cannot be detected and which therefore cannotbe classified by energy dispersive X-ray fluorescence analysis (EDXRFA)alone.

It has now been found, surprisingly, that it is also in fact possible toemploy energy dispersive X-ray fluorescence analysis for classifying andidentifying chemical substances with atomic numbers 1 to 20,specifically by the application of multivariate statistical methods tothe measurement signals obtained for the entire Compton and Rayleighscattering range.

Previously, these substances whose X-ray fluorescence lines cannot bedetected, but which only have a Compton and Rayleigh scattering range,could not be discriminated from one another but were instead assignedtogether to an allocation field. If it was desired to ascertain moreaccurately which individual elements or substances were present, it wasnecessary to carry out other conventional analyses.

The invention therefore relates to a method for classifying andidentifying, by means of energy dispersive X-ray fluorescence analysis,chemical substances whose X-ray fluorescence lines cannot be detectedand which therefore cannot be classified by energy dispersive X-rayfluorescence analysis (EDXRFA) alone, which is characterized in that thesample to be analysed is

a) positioned in front of the measurement opening in a sample chamber inan X-ray fluorescence system, then measured, and

b) classified and identified by application of multivariate statisticalmethods to the measurement signals obtained, i.e. for the Compton andRayleigh scattering range,

in its original packaging or per se without prior processing in a samplevessel.

As multivariate statistical methods, principal component analysis (PCA)is applied for detecting differences of the substances and/orregularized discriminance analysis (RDA) is applied for discriminatingand classifying the substances.

As already mentioned, checking the identity of laboratory chemicals isparticularly important when chemicals are taken back. As statisticsshow, it is primarily small packagings which are sent back in largequantities to chemical plants. Substances in small packagings aretherefore often documented and analysed in small-packaging sortingsystems (SSS). In the case of analysing different substance streams,each individual substance needs to be analysed in its packaging. Openingthe packaging and taking a sample must not be performed in the roomwhere the sorting system is and the analyses are carried out, because ofthe risk to people and the environment which should be avoided whendealing with old chemicals (Circular economy law and waste avoidance law(Krw-/AbfG) 1994 (BGBl. I, 1354); regulation No. 259/93 of the councilfor monitoring and control of the movement of waste in, into and out ofthe European Community 1993 (ABl. L 30, 1); regulation governing theintroduction of the European waste catalogue (EAK regulation) 1996(BGBl. I, 1428); regulation governing the determination of wasterequiring special monitoring (BestbüAbfV) 1996 (BGBl. I 1366);regulation governing the determination of recyclable waste requiringspecial monitoring (BestbüAbfV) 1996 (BGBl. I 1377); second generaladministrative order concerning the waste law (TA Abfall) 1991 (GMBl. p.139, corr. 496); regulation governing the protection of hazardoussubstances (GefStoffV) 1993 (BGBl. I 1782)). Energy dispersive X-rayfluorescence analysis (EDXRFA) has crystallized as a suitable method foranalysis through unopened packaging.

The analysis is hence preferably carried out through the packaging, inwhich case a variety of packaging materials (glass or polyethylenepackaging) may be present and need to be taken into considerationcorrespondingly during the allocation.

When inspecting these substances, complete identification of thesubstances including information about the main and subsidiaryconstituents is not required. Plausible allocation of the substancespectrum recorded through the packaging to the spectrum of the substancename written on the packaging label is, however, expected. This type ofanalysis is referred to as allocation analysis.

Substances which contain elements with an atomic number (AN)>22 (Ti)can, depending on the packaging size, be characterized through PEpackaging with the aid of their element lines. The peak detection, peakparameter determination (peak position, width at half maximum and area)as well as the subsequent checking of the XRFA data against theinformation in the database, take place automatically. A furtherinnovation is that these substance groups can also be discriminatedsubstantially better using multivariate statistical methods. To thatend, the X-ray fluorescence range of the element with an AN>22 and theCompton and Rayleigh scattering range are calculated using themultivariate statistical methods.

As already mentioned, however, elements with atomic numbers between 1and 20, which do not have detectable X-ray fluorescence lines, make up alarge proportion of substances. It is not possible to characterize theseelements, and therefore the substance (e.g. between NaCl and NaCN orK₂CO₃ and KF), using conventional EDXRFA evaluation owing to the lack ofX-ray fluorescence lines. The XRFA measurement of such substances onlyprovides scattered-radiation spectra which do not permit evaluation withconventional EDXRFA evaluation. These substances have thereforepreviously been assigned to a common “allocation field”.

This means, however, that only very rough assignment can take placehere. For more accurate classification, other analysis methods must beemployed in these cases, sometimes with prior sampling and processing ofthe sample. These further examinations are, however, time-consuming andexpensive. These additional analyses can now be avoided through thepresent invention.

According to the invention, further discrimination by the application ofmultivariate statistical methods to the Compton and Rayleigh scatteringrange can now be carried out for these substances, within the allocationfield which is defined with conventional X-ray fluorescence (XRFA)evaluation.

Principal component analysis (PCA) and regularized discriminanceanalysis (RDA) methods are preferred as multivariate statistical methodsfor this. These methods are known per se to the person skilled in theart, and are dealt with at length in many literature references (anexample citation for RDA is: J. H. Friedman, J. Amer. StatisticalAssociation, 1989, Vol. 84, No. 405, 165-175).

By the direct application of multivariate statistical methods to theCompton and Rayleigh scattering range, further allocation can be carriedout. According to the invention, the different methods can respectivelybe applied individually to the scattering spectra, or alternatively bothin succession.

With the aid of principal component analysis, further subclasses becomedetectable in the classes defined by the conventional XRFA evaluation.With PCA, spectral differences of the individual substances can be madevisible in the PCA representation. In Example 1 (FIG. 1), the result ofthe PCA evaluation is represented in the form of a “score plot”. Here,the scattered-radiation range of 20 substances (in a PE sample vessel),which do not give X-ray fluorescence signals, was calculated byprincipal component analysis.

With discriminance analysis methods, mathematical models are set up forthe substance classes—substances can subsequently be allocated to aclass using these models.

Classes of recorded spectra of different substances are visualised usingPCA, then their classes are calculated using RDA. This means that boththe spectral range, or the principal components calculated for thespectral range, can be used as variables in the RDA. The number ofprincipal components used is determined using the so-called “eigen value1 criterion” or by cross-validation.

The individual steps from the spectrum recording to the classificationare described below.

The sample to be analysed is firstly positioned in front of themeasurement opening in a sample chamber in the X-ray fluorescencesystem, in its original packaging—it is hence unnecessary to open thepackaging and sampling is superfluous—or per se without prior processingin a sample vessel.

The packaging or the sample vessel containing the sample to be analysedmay consist of a material selected from the group polyethylene, glass,aluminium, paper and cardboard.

The EDXRFA spectrum is now recorded. Then, if X-ray fluorescence linesof the elements in the substance are not present, the Compton andRayleigh scattering range from 19.6 to 26.3 keV (note: this range onlyapplies to excitation using an Ag tube, see Table 1; when otherexcitation sources are used, the scattering range lies in a rangecorresponding to the excitation source) is sought for the multivariatestatistical calculations (PCA, RDA). Next—if desired—the principalcomponents are calculated using PCA for the new substance in the newmodel (inc. substance) which contains the spectrum of the substance.This step is optional.

In the next step, the classes are compiled and defined in RDA with alearning data record (spectral ranges or optionally the principalcomponents identified in the previous step).

This is followed by classification/assignment of the new substance (testdata record, i.e. spectral range or principal components) to a classfrom the learning data record (optionally, the principal components fromthe PCA can also be employed for the classification, instead of thespectrum). The learning data record must naturally contain the targetclass. The classification is carried out using the calculationsdescribed in the literature. The following literature references may becited as examples for this:

Friedman, J. H., “Regularized Discriminant Analysis” in J. Am. Stat.Assoc. (1989) 84, 165-175; Frank, I. E., Friedman, J. H.,Classification: “oldtimers and newcomers” in J. Chemom. (1989) 33,463-75; Wu, W., Mallet, Y., Walczak, B., Penninckx, W., Massart, D. L.,Heuerding, S., Erni, F., “Comparison of regularized discriminantanalysis, linear discriminant analysis and quadratic discriminantanalysis, applied to NIR data” in Anal. Chim. Acta (1996) 3293, 257-265;Baldovin, A., Wen, W., Massart, D. L., Turello, A. “Regularizeddiscriminant analysis RDA—Modeling for the binary discrimination betweenpollution types” in Chemom. Intell. Lab. Syst. (1997) 381, 25-37.

The last step involves comparison between the class allocated to thespectrum and the actual class, or the substance name written on thelabel. If the result matches, the substance is processed further bybeing stored or used in production.

All this evaluation is preferably carried out automatically by applyingcorrespondingly tailored software, which substantially accelerates theentire analysis duration and calculation time. The PCA and RDA algorithmis commercially available and can be implemented in the subsequentevaluation data processing.

The substances to be analysed, in their packaging, normally reach theEDXRFA system on a conveyor belt. The positioning of the packaging infront of the measurement opening, the recording of the EDXRFA spectrum,the evaluation of the spectra, the subsequent spectrum allocation andthe repositioning of the packaging on the conveyor belt are carried outfully automatically. Accordingly, the EDXRFA system and the associatedcomponents (sample chamber, interfaces with the substance database,EDXRFA control) must be configured so that automatic operation of theindividual components is possible.

The invention therefore also relates to the use of the method within anautomated system for sorting and allocating old or new packagings whichcontain chemical substances.

The automated system preferably consists of the following components orsteps:

conveyor belt for the substances to be analysed in their packaging;

EDXRFA system;

positioning the packaging in front of the measurement opening in asample chamber, the sample chamber fully enclosing the packaging;

measurement;

spectrum evaluation and allocation;

further evaluation by application of multivariate statistical methods,and

repeated, more accurate allocation;

repositioning the packaging on the conveyor belt.

Preferably, an X-ray fluorescence analysis apparatus, consisting of anX-ray tube, a generator, an energy-resolving detector and evaluationelectronics, is used.

In a preferred embodiment, the following configuration of the EDXRFAsystem is selected: X-ray tube with generator and semiconductordetector, the measurement geometry, i.e. the angle between theexcitation source, the sample and the detector, is selected variablybetween 45° and 90°, so that the Compton and Rayleigh scattering linesare resolved in the detector.

The sample chamber must fully enclose the packaging since, when dealingwith ionizing radiation, it is necessary to comply with protectivemechanisms according to the X-ray regulation. It is necessary to ensurethat the emerging X-radiation does not exceed a defined limit value. Thesample chamber is preferably made of a material which does not increasethe spectrum background (scattering) in the sample chamber, isautomatically openable and closable, and is adaptable to the EDXRFAapparatus.

The parameters for routine operation, i.e. the X-ray tube voltage andcurrent, the primary beam filter material and thickness, the detectordiaphragm aperture, the positional coordinates for the packaging infront of the EDXRFA measurement opening, can be experimentallydetermined and adjusted as desired and according to requirements. Forsubsequent allocation of the spectra to the substances, the packagingsizes and materials and the packaging positions should also respectivelybe taken into account.

Since it is necessary to cope with a large number of packages beingtaken back, the analysis duration should be of short length. Accordingto the invention, the measurement time for recording the spectra ispreferably ≦30 seconds.

Example A, Table 1 describes a preferred configuration with theparameters for measurement and evaluation. This list is intended merelyas an entirely non-limiting example. Example A also lists the preferredgeneral measurement conditions for the trials.

Diagram 1 shows the EDXRFA measurement geometry and coordinate systemfor the packaging positioning for a preferred embodiment according tothe invention.

The method according to the invention provides a fast, reliable andeffective analysis method for identifying chemical substances throughthe packaging. EDXRFA is in principle extended to those substancescomprising elements with atomic numbers between 1 and 20 which could notpreviously be discriminated in this way. Substantially improvedcharacterization and allocation can therefore be achieved when takingback chemicals, without having to employ other analysis methods withlaborious sample processing and sampling.

Even without further embodiments, it is assumed that a person skilled inthe art can use the above description in a very wide scope. Thepreferred embodiments should therefore merely be regarded as descriptivedisclosure which is by no means limiting in any way.

The complete disclosures of all applications and publications citedabove and below are incorporated in this application by reference.

The following examples are only intended to explain the inventionfurther.

EXAMPLE A

Table 1 describes the configuration, with the parameters for measurementand evaluation, which was employed in the following examples.

TABLE 1 EDXRFA system equipment for waste-specific allocation whentaking back substances Model Description FK 60-20 Fine focus tube withAg anticathode, 60 kV, 60 mA GI-XRF02 Adjustable-level instrumentincluding screening and safety devices for holding the Si(Li)semiconductor detector, collimator and the tube protective cap C-42865Si(Li) semiconductor detector, active area: 80 mm², resolution: ≦ 165 eVfor 5.9 keV at 2000 pulses/s, preamplifier with FET input stage, pulsedoptoelectronic feedback, immersion cryostat with 25 μm Be input window,30 l liquid-gas container RS-3001 X-ray generator, tube cap with awindow, 5 m HV cable, adapter and tube cap frame RACK-300 Analogueelectronics in 19″ rack with analogue power pack, active-filteramplifier with triangle pulse shaping, baseline restorer, pulse pileuprejecter and pulse spreader, high-voltage detector supply, continuouslyadjustable from 0-1000 V TSI-MCA Analogue/digital converter and pulselevel analyser for ISA bus TSI-AQL Program package for EDXRFA under MSWindows ™ ICP-300A Industry PC in 19″ case with 250 W power pack,auxiliary fan, passive bus board with 8 ISA, 2 ISA/PCI and 4 PCI ports,single- board computer with Pentium/133 CPU, 256 kB cache, 32 MB RAM, 2× ser., 1 × par. interface E-IDE interface, 1.44 MB floppy disk drive,1.2 GB hard disk, CD-ROM drive, Matrox-Millennium PCI graphics card (2MB), MF keyboard, MS mouse, MS DOS 6.22 and MS Windows 3.11 ™ EIZO-57SSVGA colour video monitor with 43.2 cm (17″) format CRT, TOC 95 modelEizo Flexscan T57S Device cabinet in 19″ standard for holding the X-raygenerator, the system electronics and the industry PC Primary beamcollimator (3 mm beam diameter, material: Al) Primary beam filter(materials Cu, Mo, Ag) WECO KL Circulation cooler (from GWK) 04 SCAN forSoftware package for chemometric Windows ®, classification ofsubstances, from Minitab Version 1.1

The measurement conditions for the substance classification trials maybe selected as follows:

Tube high voltage 45 kv Tube current 4 mA Primary beam collimatordiameter 2 mm Primary beam filter material Ag Primary beam filterthickness 0.12 mm Detector diaphragm material Al Detector diaphragmaperture 3 mm Detector time constant 2 μs Height X-ray beam - packagingbottom 1.5 cm Measurement time for the classification trials 20 sPackaging position in front of the EDXRFA 10/0 (in mm, measurementopening right of detector centre) Angle (primary beam - sample -detector) ca. 60°

EXAMPLE 1

The measurement parameters and conditions can be found in Example A.

1.1

The Compton and Rayleigh scattered-radiation range (19.7-26.2 keV) of 20substances, which do not give an X-ray fluorescence signal, arecalculated using principal component analysis (PCA). FIG. 1 representsthe result of the PCA evaluation in the form of a “score plot”.

1.2

The results of the RDA (regularized discriminance analysis) calculationfor the 20 substances measured in 1.1 are represented in FIG. 2(calculated with the Compton and Rayleigh scattering range) and in FIG.3 (calculated with the first three principal components from the PCA).

1.3

Table 2 lists the various substances from this measurement series withtheir physical data.

TABLE 2 Various substances and their physical data from the no-elementgroup (measurement series 1) Substance M/g/mol Average AN Density/g/cm³(NH₄)₂CO₃ 96.09 6.869 1.6 (NH₄)₂SO₄ 132.14 9.301 1.766 NH₄Cl 53.4913.176 1.531 H₃BO₃ 61.83 7.133 1.435 BeSO₄ × 4H₂O 177.14 8.925 1.713Na₂CO₃ 105.99 9.075 2.533 Na₂SO₄ 142.04 10.777 2.698 NaCN 49.01 8.6311.546 NaF 41.99 10.095 2.79 NaCl 58.44 14.641 2.163 MgSO₄ × 7H₂O 246.489.034 1.68 MgCl₂ × 6H₂O 203.31 11.201 1.57 K₂CO₃ 138.21 14.049 2.428K₂SO₄ 174.27 14.407 2.662 KCN 65.12 14.020 1.56 KF 58.1 15.729 2.49 KCl74.56 18.047 1.984 CaCl₂ × 2H₂O 147.02 15.420 1.85 CaSO₄ × 2H₂O 172.1712.119 2.32 CaCO₃ 100.09 12.565 2.95

EXAMPLE 2

The measurement parameters and conditions can be found in Example A.

2.1

In this example, 5 chromium compounds were studied. Both theelement-line and the Compton and Rayleigh scattering ranges wereevaluated using principal component analysis (PCA). FIG. 4 representsthe result of the PCA evaluation in the form of a “score plot”.

2.2

The results of the RDA (regularized discriminance analysis) calculationfor this test series (see section 1.2) are represented in FIGS. 5 and 6.

2.3

Table 3 lists the studied substances from the chromium group and theirphysical data.

TABLE 3 Studied substances from the chromium group and their physicaldata (measurement series 2) Substance M/g/mol Average AN Density/g/cm³(NH₄)₂CrO₄ 152.07 12.153 1.86 K₂CrO₇ 294.19 16.579 2.69 KCr(SO₄)₂ ×12H₂O 499.41 11.215 1.83 Cr₂O₃ 151.99 18.947 5.21 CrCl₃× 6H₂O 266.4514.397 2.76

EXAMPLE 3

3.1

Measurement series 3 was carried out with substances from the iron groupusing the measurement parameters and conditions described in Example A.

Seven iron compounds were studied in this example. Both the element-lineand the Compton and Rayleigh scattering ranges were evaluated usingprincipal component analysis (PCA). FIG. 7 represents the result of thePCA evaluation in the form of a “score plot”.

3.2

The results of the RDA (regularized discriminance analysis) calculationfor this test series (see section 1.2) are represented in FIGS. 8 and 9.

3.3

Table 4 shows the physical data of this test series.

TABLE 4 Studied substances from the iron group and their physical data(measurement series 3) Substance M/g/mol Average AN Density /g/cm³ FeSO₄× 7H₂O 278.02 12.183 1.89 (NH₄)₂Fe(SO₄)₂ × 6H₂O 392.14 11.440 1.86K₄[Fe(CN)₆] × 3H₂O 422.39 13.812 1.85 K₃[Fe(CN)₆] 329.26 14.279 1.894FeCl₃ × 6H₂O 270.3  14.947 2.804 Fe₂O₃ 159.69 20.591 5.52 Fe  55.85 267.87

The examples with the PCA calculations show that, in the novel dataspace which is spanned by the principal components, 20 substances fromthe no-element group and selected substances from the chromium and ironone-element groups can be separated from one another. This separation isnot achieved with conventional EDXRFA evaluation of the spectra recordedthrough the packaging.

The best possible separation of the substances in the novel data space,and the smallest scatter within the groups, is achieved for theno-element group by PCA calculation when taking the Compton and Rayleighscattering range into account. For the one-element groups, goodseparation of the groups from one another and small scatter within thegroups is achieved by PCA calculation using a combination of thefluorescence-line range of the element and the Compton and Rayleighscattering range.

The examples with RDA calculations show that EDXRFA spectra recordedthrough the packaging can be discriminated from one another, with theaid of the relevant RDA model, if the spectra for the RDA havedetectable spectral similarities. For the chromium and iron one-elementgroups, it is possible to allocate the substances to a previouslydefined class both using the spectral range (fluorescence-line, Comptonand Rayleigh scattering range) and using the significant PCs from thePCA as variables. For the iron group, classification using the spectralrange is actually better (calculated by internal cross-validation of theiron data record).

The RDA classification of the substances of the no-element group totheir classes functions similarly.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a score plot of 20 substances, measured in the same PEcontainer (white, 80 ml), spectrum detail: Compton and Rayleighscattering range, measurement time: 20 s, PC: principal component.

FIG. 2 shows a graphical representation of the RDA results of 20substances (20 classes) of the test data record in Table 2, calculatedusing the SCAN for Windows® software, measurement time: 20 s, variables:Compton and Rayleigh scattering range.

FIG. 3 shows a graphical representation of the RDA results of 20substances (20 classes) of the test data record in Table 2, calculatedusing the SCAN for Windows® software, measurement time: 20 s, variables:PC 1 to PC 3.

FIG. 4 shows a score plot of the 5 Cr compounds, element-line andCompton and Rayleigh scattering range, measurement time: 20 s.

FIG. 5 shows a graphical representation of the RDA results of the fivechromium compounds (5 classes) in Table 3 (classification check usingcross-validation), calculated using the SCAN for Windows® software,measurement time: 20 s, variables: spectral ranges.

FIG. 6 shows a graphical representation of the RDA results of the fivechromium compounds (5 classes) in Table 3 (classification check usingcross-validation), calculated using the SCAN for Windows® software,measurement time: 20 s, variables: PC 1 to PC 3.

FIG. 7 shows a score plot of the 7 Fe compounds, element-line andCompton and Rayleigh scattering range, measurement time: 20 s.

FIG. 8 shows a graphical representation of the RDA results of the seveniron compounds (7 classes) in Table 4 (classification check usingcross-validation), calculated using the SCAN for Windows® software,measurement time: 20 s, variables: spectral ranges.

FIG. 9 shows a graphical representation of the RDA results of the seveniron compounds (7 classes) in Table 4 (classification check usingcross-validation), calculated using the SCAN for Windows® software,measurement time: 20 s, variables: PC 1 to PC 3.

FIG. 10 shows an examples of a system for carrying out the method of theinvention. The system includes (1) a primary beam collimator with filterframe (material: Al), (2) a detector diaphragm frame (material: Al), (3)a detector, (4) a Si(Li) crystal and (5) a EDXFRA aperture casing(material: Al, 10 mm).

What is claimed is:
 1. A method for classifying and identifying, by means of energy dispersive X-ray fluorescence analysis, chemical substances whose X-ray fluorescence lines cannot be detected and which therefore cannot be classified by energy dispersive X-ray fluorescence analysis (EDXRFA) alone, which comprises: a) positioning a sample to be analyzed in front of a measurement opening in a sample chamber in an X-ray fluorescence system, b) measuring the scattering of X-rays signals from the sample, and c) classifying and identifying the chemical substances, whose X-ray fluorescence lines cannot be detected, by application of principal component analysis (PCA) and/or regularized discriminance analysis (RDA) methods to the X-ray Compton and Rayleigh scattering spectral ranges of the measured signals in b), wherein the sample is maintained in its original packaging or is otherwise analyzed without prior processing in a sample vessel.
 2. The method of claim 1, wherein, when positioning the sample to be analyzed, the angle between the excitation source, the sample and the detector, also referred to as the measurement geometry, is selected variably between 45° and 90°, so that the Compton and Rayleigh scattering lines are resolved in the detector.
 3. The method of claim 1, wherein the sample to be analysed is measured and classified in its closed original packaging.
 4. The method of claim 3, wherein the original packaging of the sample and/or the sample vessel comprises a material selected from the group consisting of polyethylene, glass, aluminum, paper and cardboard.
 5. The method of claim 1, wherein the spectral differences of the individual substances are made visible in the PCA representation by the application of principal component analysis.
 6. The method of claim 1 wherein the classification/identification of the sample is carried out by application of regularized discriminance analysis (RDA) such that substance identification is made by direct calculation of a spectrum or spectral range, the test substance then being allocated to a previously determined and defined class.
 7. The method of claim 1, wherein the RDA method is applied, for classification/identification, to the principal components obtained by PCA.
 8. The method of claim 1, wherein an X-ray fluorescence analysis apparatus, comprises of an X-ray tube, a generator, an energy-resolving detector and evaluation electronics, is used to carry out the method.
 9. The method of claim 1, wherein the measurement time for recording the spectrum of a sample is ≦30 seconds.
 10. The method of claim 1, wherein the method is conducted within an automated system for sorting and allocating old or new packagings which contain chemical substances.
 11. The method of claim 10, wherein the automated system comprises the following components or steps: conveying on a conveyor belt the substances to be analysed in their packaging; an EDXRFA system; positioning the packaging in front of the measurement opening in a sample chamber, the sample chamber fully enclosing the packaging; measuring the X-ray spectrum; evaluating the spectrum and making an allocation; further evaluating by application of multivariate statistical methods, and repeated, more accurate allocation; repositioning the packaging on the conveyor belt. 